1. Technical Field
The present invention relates to License Plate Image Review systems and methods for reading large numbers of images of license plates highly efficiently and at high accuracy.
2. Related Background Art
Roads and highways are becoming increasingly more automated. On toll roads, manual toll collection is being replaced by sensors and automatic license plate readers and manual image review systems. Toll systems are being set up to control, toll and in some cases restrict traffic not just on toll roads but congested inner city regions. Automated systems are required that recognize both subscribers to the systems and non-subscribes. Non-subscribers may include visitors from different regions and occasional users of the road systems being monitored. The systems are required to cost effectively recognize a wide variety of license plates and features on thousands of cars passing daily at speeds that require high-speed photography both night and day and in all drivable weather conditions and all ambient lighting situations. The conditions of the vehicles and the plates often make images amenable to automated character recognition difficult. Enforcement of tolls requires systems that are highly reliable and systems whose results can be verified. Accuracy requirements desire license plate number recognition with error rates at low parts per thousand. High failure rates result in lost revenue, significant verification costs and customer complaints and disputes related to billing. Current systems make limited use of all of the available system information available to support highly efficient license plate recognition systems and manual reviews. Such data includes multiple appearances of the vehicle during a single trip on the tollroad, combined with past recognition data, past road usage data, and vehicle specific information. The past results in the form of verified license plate reads can be used to detect errors and improve the system. The system should be capable of self-improvement as a database of verified reads of license plates is developed. There is a need for a system that takes advantage of the abundance of data in the form of individual successful and unsuccessful license plate reads that are often available. The system should be able to provide a confidence estimate for the read of a license plate and automatically improve this estimate with experience. The system should be able to be self-improving with respect to its own accuracy of license plate reads. Most optical character recognition (“OCR”) techniques on the market today only process the gray-scale information in images, removing any color information from color images prior to processing. The system should make use of this color information to improve both automated and manual image processing efficiency and accuracy.
There is a need for an improved license plate reading system that is capable of error rates in the low part per thousand or better. There is a need for a system that judiciously uses manual verification. There is a need for a system that is self-improving over time using past data to improve future reads. There is a need for a system to empirically determine a confidence estimate for an individual read of a licenses plate and to use that experience to improve the read of the same license plate in a future traversal of the sensors and to improve the reading of other licenses plates through more accurate estimates of confidence in a read even of a different vehicle.